01764447

Component

https://doi.org/10.1177/03611981211004974

Pavement distress assessment is a significant aspect of pavement management. Automated pavement crack detection is a challenging task that has been researched for decades in response to complicated pavement conditions. Current pavement condition assessment procedures are extensively time consuming, expensive, and labor-intensive. The primary goal of this paper is to develop a cost-effective and reliable platform using a red, green, blue, depth (RGB-D) sensor and deep learning detection models for automated pavement crack detection on a single-board ARM-based computer. To the best of our knowledge, for the first time, a pavement crack data set is prepared using a global shutter RGB-D sensor mounted on a car and annotated according to the Pascal visual object classes protocol, named PAVDIS2020. The proposed data set comprises 2,085 pavement crack images that are captured in a wide variety of weather and illuminance conditions with 5,587 instances of pavement cracks included in these images. A unified implementation of the Faster region-based convolutional neural networks and single shot multibox detector meta-architecture-based models is implemented to evaluate the accuracy, speed, and memory usage trade-off by using various convolutional neural networks-based backbones and various other training parameters on PAVDIS2020. The proposed pavement crack detection model was able to classify the cracks with 97.6% accuracy on PAVDIS2020 data set. The detection model is able to locate pavement crack patterns at the speed of 12 frames per second on a passively cooled Raspberry Pi 4 single-board computer.

The data set generated during and/or analyzed during the current study is proprietary or confidential in nature and may only be provided with restrictions. © National Academy of Sciences: Transportation Research Board 2021.

TRBAM-21-03273

English

pp 885-897

2021-9

Transportation Research Record: Journal of the Transportation Research Board

Digital/other

Figures; Photos; References (50) ; Tables

Automation; Detection and identification; Machine learning; Neural networks; Pavement cracking; Sensors

Data and Information Technology; Highways; Pavements

TRIS, TRB, ATRI

Dec 23 2020 11:25AM